Toronto Dwellings Analysis

In this assignment, you will perform fundamental analysis for the Toronto dwellings market to allow potential real estate investors to choose rental investment properties.

# imports
import panel as pn
pn.extension('plotly')
import plotly.express as px
import pandas as pd
import hvplot.pandas
import matplotlib.pyplot as plt
import os
from pathlib import Path
from dotenv import load_dotenv
import random

# Read the Mapbox API key
load_dotenv()
map_box_api = os.getenv("MAPBOX_API_KEY")
# Set the Mapbox API
px.set_mapbox_access_token(map_box_api)

Load Data

# Read the census data into a Pandas DataFrame
file_path = Path("Data/toronto_neighbourhoods_census_data.csv")
to_data = pd.read_csv(file_path, index_col="year")
to_data.head()

	neighbourhood	single_detached_house	apartment_five_storeys_plus	movable_dwelling	semi_detached_house	row_house	duplex	apartment_five_storeys_less	other_house	average_house_value	shelter_costs_owned	shelter_costs_rented
year
2001	Agincourt North	3715	1480	0	1055	1295	195	185	5	200388	810	870
2001	Agincourt South-Malvern West	3250	1835	0	545	455	105	425	0	203047	806	892
2001	Alderwood	3175	315	0	470	50	185	370	0	259998	817	924
2001	Annex	1060	6090	5	1980	605	275	3710	165	453850	1027	1378
2001	Banbury-Don Mills	3615	4465	0	240	380	15	1360	0	371864	1007	1163

---

Dwelling Types Per Year

In this section, you will calculate the number of dwelling types per year. Visualize the results using bar charts and the Pandas plot function.

Hint: Use the Pandas groupby function.

Optional challenge: Plot each bar chart in a different color.

# Calculate the sum number of dwelling types units per year (hint: use groupby)
#We will also drop the columns which are not the dwelling types
dwellings_per_year = to_data.groupby("year").sum().drop(columns=["average_house_value", "shelter_costs_owned", "shelter_costs_rented" ])

dwellings_per_year

	single_detached_house	apartment_five_storeys_plus	movable_dwelling	semi_detached_house	row_house	duplex	apartment_five_storeys_less	other_house
year
2001	300930	355015	75	90995	52355	23785	116900	3040
2006	266860	379400	165	69430	54690	44095	162850	1335
2011	274940	429220	100	72480	60355	44750	163895	2165
2016	269680	493270	95	71200	61565	48585	165575	2845

# Save the dataframe as a csv file
dwellings_per_year.to_csv("Data/dwelling_types_per_year.csv")

# Helper create_bar_chart function
def create_bar_chart(data, title, xlabel, ylabel, color):
    """
    Create a barplot based in the data argument.
    Input: 
    data = DataFrame to use for plotting the data
    title = Chart Title
    xlabel = Label for X Axis
    ylabel = Label for Y Axis
    color = Colour of the bar chart

    """
    data.plot(kind="bar", xlabel=xlabel, ylabel=ylabel, color=color, title=title)
    
    #We want to show the plot after plotting it, if we dont do this, it will overwrite the previously plotted bar chart.
    plt.show()

# Create a bar chart per year to show the number of dwelling types
colors = ["red", "pink", "blue", "yellow"]

#We will use a for loop to do charts for each year rather than repeating the code and we will use a random color from the colors list.
for index in dwellings_per_year.index:
    title = "Dwelling Types in Toranto in " + str(index)
    ylabel = "Dwelling Type Units"
    create_bar_chart(dwellings_per_year.loc[index], title=title, xlabel=index, ylabel=ylabel, color=random.choice(colors))

---

Average Monthly Shelter Costs in Toronto Per Year

In this section, you will calculate the average monthly shelter costs for owned and rented dwellings and the average house value for each year. Plot the results as a line chart.

Optional challenge: Plot each line chart in a different color.

# Calculate the average monthly shelter costs for owned and rented dwellings
#We will also drop the columns which are not the dwelling types
#dwellings_per_year = to_data[[]].groupby("year").mean().drop(columns=["average_house_value", "shelter_costs_owned", "shelter_costs_rented" ])

monthly_shelter_cost_per_year = to_data[["shelter_costs_owned","shelter_costs_rented"]].groupby("year").mean()
monthly_shelter_cost_per_year

	shelter_costs_owned	shelter_costs_rented
year
2001	846.878571	1085.935714
2006	1316.800000	925.414286
2011	1448.214286	1019.792857
2016	1761.314286	1256.321429

# Helper create_line_chart function
def create_line_chart(data, title, xlabel, ylabel, color):
    """
    Create a line chart based in the data argument.

    Input: 
    data = DataFrame to use for plotting the data
    title = Chart Title
    xlabel = Label for X Axis
    ylabel = Label for Y Axis
    color = Colour of the bar chart

    """
    data.plot(kind="line", xlabel=xlabel, ylabel=ylabel, color=color, title=title)
    
    #We want to show the plot after plotting it, if we dont do this, it will overwrite the previously plotted bar chart.
    plt.show()

# Create two line charts, one to plot the monthly shelter costs for owned dwelleing and other for rented dwellings per year

# Line chart for owned dwellings

create_line_chart(data=monthly_shelter_cost_per_year["shelter_costs_owned"], title="Average Monthly Shelter Cost for Owned Dwellings in Toronto ", 
                  xlabel="Year", ylabel="Average Monthly Shelter Costs", color="red") 
# Line chart for rented dwellings
create_line_chart(data=monthly_shelter_cost_per_year["shelter_costs_rented"], title="Average Monthly Shelter Cost for Rented Dwellings in Toronto ", 
                  xlabel="Year", ylabel="Average Monthly Shelter Costs", color="yellow") 

Average House Value per Year

In this section, you want to determine the average house value per year. An investor may want to understand better the sales price of the rental property over time. For example, a customer will want to know if they should expect an increase or decrease in the property value over time so they can determine how long to hold the rental property. You will visualize the average_house_value per year as a bar chart.

# Calculate the average house value per year
average_house_value = to_data["average_house_value"].groupby("year").mean()
average_house_value

year
2001    289882.885714
2006    424059.664286
2011    530424.721429
2016    664068.328571
Name: average_house_value, dtype: float64

# Plot the average house value per year as a line chart
create_line_chart(data=average_house_value, title="Average House Value in Toronto ", 
                  xlabel="Year", ylabel="Average House Value", color="blue")

---

Average House Value by Neighbourhood

In this section, you will use hvplot to create an interactive visualization of the average house value with a dropdown selector for the neighbourhood.

Hint: It will be easier to create a new DataFrame from grouping the data and calculating the mean house values for each year and neighbourhood.

# Create a new DataFrame with the mean house values by neighbourhood per year
avg_house_value_by_neighbourhood = to_data[["neighbourhood","average_house_value"]].reset_index()
avg_house_value_by_neighbourhood.head(10)

	year	neighbourhood	average_house_value
0	2001	Agincourt North	200388
1	2001	Agincourt South-Malvern West	203047
2	2001	Alderwood	259998
3	2001	Annex	453850
4	2001	Banbury-Don Mills	371864
5	2001	Bathurst Manor	304749
6	2001	Bay Street Corridor	257404
7	2001	Bayview Village	327644
8	2001	Bayview Woods-Steeles	343535
9	2001	Bedford Park-Nortown	565304

# Use hvplot to create an interactive line chart of the average house value per neighbourhood
# The plot should have a dropdown selector for the neighbourhood
avg_house_value_by_neighbourhood.hvplot.line(x="year", y="average_house_value", xlabel="Year", ylabel="Average House Value", groupby="neighbourhood")

Number of Dwelling Types per Year

In this section, you will use hvplot to create an interactive visualization of the average number of dwelling types per year with a dropdown selector for the neighbourhood.

# Fetch the data of all dwelling types per year
dwelling_types_per_year = to_data.drop(columns=["average_house_value", "shelter_costs_owned", "shelter_costs_rented" ])
dwelling_types_per_year.head(10)

	neighbourhood	single_detached_house	apartment_five_storeys_plus	movable_dwelling	semi_detached_house	row_house	duplex	apartment_five_storeys_less	other_house
year
2001	Agincourt North	3715	1480	0	1055	1295	195	185	5
2001	Agincourt South-Malvern West	3250	1835	0	545	455	105	425	0
2001	Alderwood	3175	315	0	470	50	185	370	0
2001	Annex	1060	6090	5	1980	605	275	3710	165
2001	Banbury-Don Mills	3615	4465	0	240	380	15	1360	0
2001	Bathurst Manor	2405	1550	0	130	130	375	745	0
2001	Bay Street Corridor	10	7575	0	0	15	0	240	0
2001	Bayview Village	2170	630	0	170	765	15	640	0
2001	Bayview Woods-Steeles	1650	1715	0	925	105	10	170	5
2001	Bedford Park-Nortown	4985	2080	0	45	40	210	1235	15

# Use hvplot to create an interactive bar chart of the number of dwelling types per neighbourhood
# The plot should have a dropdown selector for the neighbourhood

dwelling_types_per_year.hvplot.bar(groupby="neighbourhood",rot=90, ylabel="Dwelling Type Units", xlabel="Year", height=500)

---

The Top 10 Most Expensive Neighbourhoods

In this section, you will need to calculate the house value for each neighbourhood and then sort the values to obtain the top 10 most expensive neighbourhoods on average. Plot the results as a bar chart.

# Getting the data from the top 10 expensive neighbourhoods
ten_expensive_neighbourhoods = to_data.groupby("neighbourhood").mean().sort_values(by="average_house_value", ascending=False).reset_index()
ten_expensive_neighbourhoods.head(10)

	neighbourhood	single_detached_house	apartment_five_storeys_plus	movable_dwelling	semi_detached_house	row_house	duplex	apartment_five_storeys_less	other_house	average_house_value	shelter_costs_owned	shelter_costs_rented
0	Bridle Path-Sunnybrook-York Mills	2260.00	331.25	0.00	36.25	90.00	25.0	40.00	0.00	1526485.75	2360.75	2321.75
1	Forest Hill South	1742.50	2031.25	1.25	61.25	45.00	75.0	1027.50	3.75	1195992.50	1781.00	1313.75
2	Lawrence Park South	3472.50	773.75	0.00	126.25	38.75	225.0	966.25	16.25	1094027.75	1954.00	1372.75
3	Rosedale-Moore Park	2498.75	4641.25	0.00	486.25	245.00	327.5	1618.75	2.50	1093640.00	1909.75	1537.25
4	St.Andrew-Windfields	3225.00	1670.00	0.00	185.00	552.50	97.5	586.25	5.00	999107.00	1880.25	1384.50
5	Casa Loma	916.25	2310.00	0.00	288.75	201.25	162.5	1192.50	2.50	981064.25	1873.75	1547.75
6	Bedford Park-Nortown	4865.00	1981.25	0.00	43.75	57.50	287.5	1275.00	88.75	930415.25	1786.75	1255.00
7	Forest Hill North	1488.75	3392.50	0.00	12.50	16.25	82.5	402.50	1.25	851680.50	1722.75	1245.50
8	Kingsway South	2326.25	576.25	0.00	66.25	48.75	20.0	336.25	2.50	843234.25	1736.75	1622.00
9	Yonge-St.Clair	565.00	3948.75	0.00	425.00	212.50	172.5	1308.75	6.25	813220.25	1680.75	1369.00

# Plotting the data from the top 10 expensive neighbourhoods
ten_expensive_neighbourhoods.head(10).hvplot.bar(rot=90, ylabel="Average House Value", xlabel="Neighbourhood", y="average_house_value", x="neighbourhood",height=500)
#ten_expensive_neighbourhoods[["average_house_value","shelter_costs_rented"]]

---

Neighbourhood Map

In this section, you will read in neighbourhoods location data and build an interactive map with the average house value per neighbourhood. Use a scatter_mapbox from Plotly express to create the visualization. Remember, you will need your Mapbox API key for this.

Load Location Data

# Load neighbourhoods coordinates data
file_path = Path("Data/toronto_neighbourhoods_coordinates.csv")
df_neighbourhood_locations = pd.read_csv(file_path)
df_neighbourhood_locations

	neighbourhood	lat	lon
0	Agincourt North	43.805441	-79.266712
1	Agincourt South-Malvern West	43.788658	-79.265612
2	Alderwood	43.604937	-79.541611
3	Annex	43.671585	-79.404001
4	Banbury-Don Mills	43.737657	-79.349718
...	...	...	...
135	Wychwood	43.676919	-79.425515
136	Yonge-Eglinton	43.704689	-79.403590
137	Yonge-St.Clair	43.687859	-79.397871
138	York University Heights	43.765736	-79.488883
139	Yorkdale-Glen Park	43.714672	-79.457108

140 rows × 3 columns

Data Preparation

You will need to join the location data with the mean values per neighbourhood.

1. Calculate the mean values for each neighbourhood.

2. Join the average values with the neighbourhood locations.

# Calculate the mean values for each neighborhood
mean_data_neighbourhoods = to_data.groupby("neighbourhood").mean().reset_index()
mean_data_neighbourhoods.head()

	neighbourhood	single_detached_house	apartment_five_storeys_plus	movable_dwelling	semi_detached_house	row_house	duplex	apartment_five_storeys_less	other_house	average_house_value	shelter_costs_owned	shelter_costs_rented
0	Agincourt North	3435.00	1947.50	2.50	863.75	1406.25	512.50	547.50	10.00	329811.5	1109.00	983.50
1	Agincourt South-Malvern West	2897.50	2180.00	1.25	375.00	456.25	523.75	628.75	32.50	334189.0	1131.25	985.00
2	Alderwood	2903.75	302.50	1.25	503.75	76.25	302.50	502.50	1.25	427922.5	1166.75	1003.25
3	Annex	751.25	7235.00	1.25	1375.00	613.75	355.00	4605.00	83.75	746977.0	1692.75	1315.25
4	Banbury-Don Mills	3572.50	5388.75	1.25	273.75	626.25	32.50	1340.00	0.00	612039.0	1463.50	1242.75

#Set the index to neighbourhood so we join without any errors or inaccuracies
mean_data_neighbourhoods.set_index(keys="neighbourhood",inplace=True)
df_neighbourhood_locations.set_index(keys="neighbourhood",inplace=True)

# Join the average values with the neighbourhood locations
neighbourhood_with_location = pd.concat([mean_data_neighbourhoods, df_neighbourhood_locations ], join="inner", axis="columns")
neighbourhood_with_location

	single_detached_house	apartment_five_storeys_plus	movable_dwelling	semi_detached_house	row_house	duplex	apartment_five_storeys_less	other_house	average_house_value	shelter_costs_owned	shelter_costs_rented	lat	lon
neighbourhood
Agincourt North	3435.00	1947.50	2.50	863.75	1406.25	512.50	547.50	10.00	329811.50	1109.00	983.50	43.805441	-79.266712
Agincourt South-Malvern West	2897.50	2180.00	1.25	375.00	456.25	523.75	628.75	32.50	334189.00	1131.25	985.00	43.788658	-79.265612
Alderwood	2903.75	302.50	1.25	503.75	76.25	302.50	502.50	1.25	427922.50	1166.75	1003.25	43.604937	-79.541611
Annex	751.25	7235.00	1.25	1375.00	613.75	355.00	4605.00	83.75	746977.00	1692.75	1315.25	43.671585	-79.404001
Banbury-Don Mills	3572.50	5388.75	1.25	273.75	626.25	32.50	1340.00	0.00	612039.00	1463.50	1242.75	43.737657	-79.349718
...	...	...	...	...	...	...	...	...	...	...	...	...	...
Wychwood	1056.25	1236.25	0.00	992.50	298.75	325.00	1878.75	17.50	565976.50	1390.75	1017.25	43.676919	-79.425515
Yonge-Eglinton	1468.75	1638.75	0.00	470.00	33.75	328.75	1385.00	6.25	809745.75	1799.50	1347.75	43.704689	-79.403590
Yonge-St.Clair	565.00	3948.75	0.00	425.00	212.50	172.50	1308.75	6.25	813220.25	1680.75	1369.00	43.687859	-79.397871
York University Heights	1355.00	5165.00	1.25	1316.25	662.50	188.75	1085.00	33.75	305899.50	1116.75	937.50	43.765736	-79.488883
Yorkdale-Glen Park	2286.25	1347.50	0.00	73.75	450.00	377.50	722.50	7.50	430861.25	1122.50	942.50	43.714672	-79.457108

140 rows × 13 columns

Mapbox Visualization

Plot the average values per neighbourhood using a Plotly express scatter_mapbox visualization.

# Create a scatter mapbox to analyze neighbourhood info
avg_house_value_map= px.scatter_mapbox(
    neighbourhood_with_location,
    lat="lat",
    lon="lon",
color="average_house_value", title="Average House Value in Toronto"
)
avg_house_value_map.show()

---

Cost Analysis - Optional Challenge

In this section, you will use Plotly express to a couple of plots that investors can interactively filter and explore various factors related to the house value of the Toronto's neighbourhoods.

Create a bar chart row facet to plot the average house values for all Toronto's neighbourhoods per year

mean_data_neighbourhoods.reset_index(inplace=True)

to_data

	neighbourhood	single_detached_house	apartment_five_storeys_plus	movable_dwelling	semi_detached_house	row_house	duplex	apartment_five_storeys_less	other_house	average_house_value	shelter_costs_owned	shelter_costs_rented
year
2001	Agincourt North	3715	1480	0	1055	1295	195	185	5	200388	810	870
2001	Agincourt South-Malvern West	3250	1835	0	545	455	105	425	0	203047	806	892
2001	Alderwood	3175	315	0	470	50	185	370	0	259998	817	924
2001	Annex	1060	6090	5	1980	605	275	3710	165	453850	1027	1378
2001	Banbury-Don Mills	3615	4465	0	240	380	15	1360	0	371864	1007	1163
...	...	...	...	...	...	...	...	...	...	...	...	...
2016	Wychwood	920	1295	0	880	290	395	2080	35	787760	1864	1146
2016	Yonge-Eglinton	1400	1995	0	465	60	310	1445	0	1127052	2398	1535
2016	Yonge-St.Clair	520	4315	0	450	220	130	1370	0	1131888	2192	1619
2016	York University Heights	1235	5505	0	1360	775	280	995	0	425769	1444	1122
2016	Yorkdale-Glen Park	2165	1185	0	80	600	465	830	5	599698	1451	1128

560 rows × 12 columns

px.bar(to_data, x="neighbourhood", y="average_house_value",color="average_house_value", facet_row=to_data.index,height=1000, title="Average House Values in Toronto Per Neighbourhood" )

Create a sunburst chart to conduct a costs analysis of most expensive neighbourhoods in Toronto per year

# Fetch the data from all expensive neighbourhoods per year.
ten_most_expensive_neighbourhoods_per_year = to_data.sort_values(by="average_house_value", ascending=False).groupby('year').head(10)
ten_most_expensive_neighbourhoods_per_year

	neighbourhood	single_detached_house	apartment_five_storeys_plus	movable_dwelling	semi_detached_house	row_house	duplex	apartment_five_storeys_less	other_house	average_house_value	shelter_costs_owned	shelter_costs_rented
year
2016	Bridle Path-Sunnybrook-York Mills	2275	590	0	40	230	30	80	0	2124653	2903	2942
2011	Bridle Path-Sunnybrook-York Mills	2285	480	0	45	100	30	50	0	1697067	2387	2388
2016	Forest Hill South	1685	2025	0	65	40	70	1050	0	1664653	2383	1486
2016	Lawrence Park South	3420	925	0	110	35	230	1015	5	1522732	2644	1515
2016	Rosedale-Moore Park	2450	4990	0	485	260	345	1505	5	1522193	2498	1809
2016	St.Andrew-Windfields	3245	1745	0	175	540	120	595	5	1390616	2516	1565
2016	Casa Loma	875	2680	0	265	200	155	1225	0	1365503	2434	1841
2006	Bridle Path-Sunnybrook-York Mills	2205	145	0	35	15	30	10	0	1356757	2170	2167
2011	Forest Hill South	1730	1825	0	65	45	75	1035	0	1329642	1959	1206
2016	Bedford Park-Nortown	4820	1995	0	45	65	315	1260	115	1295007	2418	1385
2011	Lawrence Park South	3465	855	0	115	25	230	980	10	1216283	2174	1230
2011	Rosedale-Moore Park	2485	4905	0	495	250	345	1550	0	1215852	2054	1468
2016	Forest Hill North	1470	3430	0	15	30	110	385	0	1185419	2316	1395
2016	Kingsway South	2310	790	0	70	50	30	335	0	1173663	2173	2020
2016	Yonge-St.Clair	520	4315	0	450	220	130	1370	0	1131888	2192	1619
2011	St.Andrew-Windfields	3285	1740	0	180	555	120	580	10	1110755	2069	1270
2011	Casa Loma	880	2630	0	245	220	140	1215	0	1090696	2001	1494
2006	Forest Hill South	1740	1835	0	50	50	70	1015	0	1063011	1781	1094
2011	Bedford Park-Nortown	4870	1960	0	40	65	335	1320	115	1034387	1988	1124
2006	Lawrence Park South	3415	745	0	110	25	250	1025	10	972384	1977	1116
2006	Rosedale-Moore Park	2450	4790	0	445	245	330	1685	5	972039	1868	1332
2011	Forest Hill North	1470	3350	0	15	25	105	365	0	946854	1904	1132
2011	Kingsway South	2350	695	0	70	50	25	335	0	937464	1787	1640
2001	Bridle Path-Sunnybrook-York Mills	2275	110	0	25	15	10	20	0	927466	1983	1790
2011	Yonge-St.Clair	530	4070	0	450	220	135	1380	0	904096	1802	1314
2006	St.Andrew-Windfields	3095	1740	0	165	560	105	645	0	888017	1881	1152
2006	Casa Loma	875	2230	0	230	195	170	1240	5	871981	1819	1356
2006	Bedford Park-Nortown	4785	1890	0	45	60	290	1285	110	826963	1808	1020
2006	Forest Hill North	1450	3410	0	10	10	115	375	0	756983	1731	1027
2006	Kingsway South	2275	560	0	60	50	25	335	0	749476	1625	1488
2001	Forest Hill South	1815	2440	5	65	45	85	1010	15	726664	1001	1469
2006	Yonge-St.Clair	540	3785	0	390	210	145	1465	0	722799	1638	1192
2001	Lawrence Park South	3590	570	0	170	70	190	845	40	664712	1021	1630
2001	Rosedale-Moore Park	2610	3880	0	520	225	290	1735	0	664476	1219	1540
2001	St.Andrew-Windfields	3275	1455	0	220	555	45	525	5	607040	1055	1551
2001	Casa Loma	1035	1700	0	415	190	185	1090	5	596077	1241	1500
2001	Bedford Park-Nortown	4985	2080	0	45	40	210	1235	15	565304	933	1491
2001	Forest Hill North	1565	3380	0	10	0	0	485	5	517466	940	1428
2001	Kingsway South	2370	260	0	65	45	0	340	10	512334	1362	1340
2001	Yonge-St.Clair	670	3625	0	410	200	280	1020	25	494098	1091	1351

# Create the sunburst chart
px.sunburst(
    ten_most_expensive_neighbourhoods_per_year,
    path=[ten_most_expensive_neighbourhoods_per_year.index, 'neighbourhood'], values='average_house_value', color='average_house_value', color_continuous_scale='blues',
    height=500
    
)